数据科学家，实验项目
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Data Scientist, Experimental ProjectsStripe · San Francisco
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职位描述
机器翻译我们是谁
关于 Stripe
Stripe 是一个面向企业的金融基础设施平台。数百万家公司——从全球最大的企业到最有抱负的初创公司——都在使用 Stripe 来接受付款、增长收入并加速新的商业机会。我们的使命是提升互联网的 GDP，而我们面前还有大量工作要做。这意味着你拥有一个前所未有的机会，在从事职业生涯中最重要工作的同时，让全球经济触手可及。
关于团队
实验项目团队会快速测试 Stripe 的新产品机会。我们通过构建原型、与用户交流、分析我们学到的东西并快速迭代，来处理全新的、从零到一的问题。
该团队在广泛的问题领域中开展工作。你不需要优化某个单一成熟产品领域，而是会帮助判断新想法是否能够解决有意义的用户问题，并成为对 Stripe 有价值的产品。我们正在寻找一位喜欢构建、行动力强，并且能够从容地从模糊问题推进到实际测试的数据科学家。
职责
• 使用数据来识别、评估并塑造新的产品机会。
• 与工程师和产品经理合作，构建并测试早期产品概念。
• 开发分析、模型、实验和原型，帮助团队快速学习。
• 与用户交流，并将定性洞察与定量证据结合起来。
• 为新想法定义成功衡量标准，并评估早期结果是否支持进一步投入。
• 跨多个新问题领域开展工作，并随着优先级和证据的变化调整你的方法。
• 清晰传达发现，包括不确定性、权衡取舍以及建议的下一步行动。
• 帮助为可能成长为更大产品领域的项目建立分析基础。
你将做什么
你将与产品经
岗位职责
• 使用数据来识别、评估并塑造新的产品机会。
• 与工程师和产品经理合作，构建并测试早期产品概念。
• 开发分析、模型、实验和原型，帮助团队快速学习。
• 与用户交流，并将定性洞察与定量证据结合起来。
• 为新想法定义成功衡量标准，并评估早期结果是否支持进一步投入。
• 跨多个新问题领域开展工作，并随着优先级和证据的变化调整你的方法。
• 清晰传达发现，包括不确定性、权衡取舍以及建议的下一步行动。
• 帮助为可能成长为更大产品领域的项目建立分析基础。
你将与产品经理、工程师、设计师以及其他跨职能合作伙伴紧密合作，探索新的产品机会。你将在整个发现和开发过程中使用数据科学，从识别有前景的问题、塑造假设，到构建早期解决方案并评估结果。
你的工作可能包括产品分析、实验、统计建模、机器学习、因果推断和快速原型制作。具体方法将取决于机会本身。要在这个职位上取得成功，需要为每个阶段选择合适程度的分析严谨性，在证据有限时快速推进，并将你学到的东西转化为关于团队接下来应该构建或测试什么的清晰建议。
任职要求
我们正在寻找符合该职位最低要求的人选。如果你符合这些要求，我们鼓励你申请。优先资格是加分项，而非必需条件。
地点要求
• San Francisco, CA（混合办公：50% 在办公室 - Oyster Point）
最低要求
• 博士学位并具备 3 年以上经验，硕士或文学硕士并具备 6 年以上经验，或学士或文学学士并具备 8 年以上数据科学或定量建模经验。
• 熟练掌握 SQL 以及一种计算语言，例如 Python 或 R。
• 能够独立工作，也能与跨学科团队（包括工程和财务）有效合作，以交付有影响力的成果。
• 已证明有能力管理并交付多个项目，且高度注重细节。
• 扎实的商业敏锐度，以及将复杂分析综合为可执行建议的经验。
• 有建立关系并影响高级技术领导层决策的记录。
• 具备构建者的心态，愿意质疑假设和传统智慧。
• 熟练使用人工智能工具来加速模型开发、分析和编码。
• 在以下若干领域具备扎实知识和实践经验：机器学习、统计学、优化、产品分析、因果推断和实验
• 有在生产环境中部署模型并调整模型阈值以提升性能的经验
• 有设计、运行和分析复杂实验或使用因果推断方法的经验
• 具备构建者的心态，并愿意质疑假设和传统智慧
• 有处理模糊的、从零到一问题的经验，并能将早期证据转化为实际决策
• 行动力强，包括能够识别测试假设的最快可信方式
• 能够自如地跨不同问题领域开展工作，并快速学习不熟悉的领域
• 拥有定量领域（如统计学、工程学、数学、经济学、定量金融、科学或运筹学）的博士学位或硕士学位
以上内容由机器翻译自动生成，可能存在错误；投递前请以雇主原文为准。
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职位描述
Who we are
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.
About the team
The Experimental Projects team quickly tests new product opportunities for Stripe. We work on brand-new, zero-to-one problems by building prototypes, talking with users, analyzing what we learn, and iterating rapidly.
The team operates across a broad range of problem spaces. Rather than optimizing a single mature product area, you’ll help determine whether new ideas can solve meaningful user problems and become valuable products for Stripe. We’re looking for a Data Scientist who enjoys building, has a strong bias for action, and is comfortable moving from an ambiguous question to a practical test.
Responsibilities
• Use data to identify, evaluate, and shape new product opportunities.
• Partner with engineers and product managers to build and test early product concepts.
• Develop analyses, models, experiments, and prototypes that help the team learn quickly.
• Talk with users and combine qualitative insights with quantitative evidence.
• Define success measures for new ideas and assess whether early results support further investment.
• Work across several new problem areas, adapting your approach as priorities and evidence change.
• Communicate findings clearly, including uncertainty, tradeoffs, and recommended next steps.
• Help establish analytical foundations for projects that may grow into larger product areas.
What you'll do
You’ll partner closely with product man
岗位职责
• Use data to identify, evaluate, and shape new product opportunities.
• Partner with engineers and product managers to build and test early product concepts.
• Develop analyses, models, experiments, and prototypes that help the team learn quickly.
• Talk with users and combine qualitative insights with quantitative evidence.
• Define success measures for new ideas and assess whether early results support further investment.
• Work across several new problem areas, adapting your approach as priorities and evidence change.
• Communicate findings clearly, including uncertainty, tradeoffs, and recommended next steps.
• Help establish analytical foundations for projects that may grow into larger product areas.
You’ll partner closely with product managers, engineers, designers, and other cross-functional partners to explore new product opportunities. You’ll use data science throughout the discovery and development process, from identifying promising problems and shaping hypotheses to building early solutions and evaluating results.
Your work may include product analytics, experimentation, statistical modeling, machine learning, causal inference, and rapid prototyping. The specific methods will depend on the opportunity. Success in this role requires choosing the right level of analytical rigor for each stage, working quickly when evidence is limited, and turning what you learn into clear recommendations about what the team should build or test next.
任职要求
We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
Location Requirement
• San Francisco, CA (Hybrid: 50% in office - Oyster Point)
Minimum requirements
• PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience.
• Proficiency in SQL and a computing language such as Python or R.
• Ability to effectively work both independently and with cross-disciplinary teams, including engineering and finance, to deliver impactful results.
• A demonstrated ability to manage and deliver on multiple projects with a high attention to detail.
• Solid business acumen and experience in synthesizing complex analyses into actionable recommendations.
• A track record of building relationships with and influencing the decisions of senior technical leadership.
• A builder's mindset with a willingness to question assumptions and conventional wisdom.
• Proficiency with artificial intelligence tools to accelerate model development, analysis, and coding.
• Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation
• Experience deploying models in production and adjusting model thresholds to improve performance
• Experience designing, running, and analyzing complex experiments or using causal inference methods
• A builder’s mindset and willingness to question assumptions and conventional wisdom
• Experience working on ambiguous, zero-to-one problems and turning early evidence into practical decisions
• A strong bias for action, including the ability to identify the fastest credible way to test a hypothesis
• Comfort moving across different problem spaces and learning unfamiliar domains quickly
• A PhD or MS in a quantitative field, such as statistics, engineering, mathematics, economics, quantitative finance, science, or operations research